Journal of Advanced Transportation最新文献

{"title":"Cognitive and Driving Performance of Individuals With a History of Concussion in Conditionally Automated Vehicles","authors":"Fabiha Islam,&nbsp;Vipul Lugade,&nbsp;Stanley Hunter,&nbsp;Max Kammerman,&nbsp;Karyssa Bowron,&nbsp;Michael Dulas,&nbsp;Mei-Hsiu Chen,&nbsp;Mark Fuchs,&nbsp;Clayton Battle,&nbsp;Chao Shi","doi":"10.1155/atr/8243477","DOIUrl":"https://doi.org/10.1155/atr/8243477","url":null,"abstract":"<p>Concussion represents a global health challenge affecting cognitive, motor, perceptual and behavioural tasks. Concussed individuals often experience impairments that impact driving performance, yet research on driving abilities, particularly in conditionally automated vehicles (CAVs), remains limited. We evaluated the cognitive and driving performances of individuals with a history of concussion in CAVs using a fixed-base driving simulator. Fourteen healthy and 14 concussed participants performed multiple takeover tasks across repeated trials while engaged in a nondriving-related task. Cognitive states, such as mental workload (MWL) and situational awareness (SA), were assessed using the NASA task load index, the SA rating technique and the situation present assessment method, while driving performance metrics included takeover time and success rates. Results indicated no significant differences between the groups’ overall MWL, SA and driving performance. However, analysis of learning effects across multiple takeovers and trials revealed that the healthy group showed significantly more improvements in SA than the concussed group. These findings suggest that while individuals with a history of concussion may not experience significant impairments in specific driving tasks or overall SA and MWL, subtle differences may persist in their ability to learn, access and process information, potentially affecting decision-making and safety. Overall, this study underscores the need for further investigations into the effects of concussion on driving CAVs and development of tailored support systems for affected individuals.</p>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2026 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/8243477","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148994569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BusSche: A Two-Step Optimization Framework for Semiflexible Bus Scheduling in Intelligent Transportation Networks","authors":"Rong-Guei Tsai,&nbsp;Chengtao Xu,&nbsp;Wen Wang,&nbsp;Yanhong Peng,&nbsp;Wulin Yin,&nbsp;Ting Yao,&nbsp;Shu Gong,&nbsp;Suping Liu","doi":"10.1155/atr/6122376","DOIUrl":"https://doi.org/10.1155/atr/6122376","url":null,"abstract":"<p>In this paper, we propose BusSche, which is a two-step optimization framework for semiflexible bus scheduling in intelligent transportation networks. We first allocate resources globally based on vehicle usage and idle time segments and then refine schedules locally to balance operational cost and passenger experience. We compare BusSche to the greedy, simulated annealing, genetic, and whale optimization algorithms. We experiment with different route scales, station densities, and fleet sizes. We show that BusSche achieves lower operating costs (30%–50%) than simulated annealing and 10%–20% lower than the genetic and whale optimization algorithms, while maintaining a stable passenger experience score around 0.16–0.17. These results suggest that BusSche is a robust and flexible solution for dynamic transit environments.</p>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2026 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/6122376","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148943236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Bivariate Finite Mixture Modeling for Analyzing Freeway Correlated Crashes Under Unobserved Heterogeneity","authors":"Mingjie Feng,&nbsp;Lulu Zhou,&nbsp;Jianluo Wei,&nbsp;Xuesong Wang,&nbsp;Xiaohan Yang,&nbsp;Jiawen Wang,&nbsp;Jaeyoung Jay Lee","doi":"10.1155/atr/8328602","DOIUrl":"https://doi.org/10.1155/atr/8328602","url":null,"abstract":"<p>Unobserved heterogeneity is a key factor leading to biased estimates and unreliable crash predictions, undermining the effectiveness of safety countermeasures. Two common options to address it are separating crash types and leveraging advanced statistical models. While finite mixture models have recently demonstrated superiority in this regard, they fail to account for correlations between different crash types. This study thus integrates the two approaches by developing bivariate finite mixture (BFM) models while decomposing crashes into single-vehicle (SV) and multivehicle (MV) crashes so as to simultaneously address the correlation feature and unobserved heterogeneity. Two variants of the BFM model are developed with SV and MV crash counts as the bivariate response variable. The first variant, BFM-I-2, measures the correlation using multivariate normally distributed random error terms, while the second, BFM-II-2, employs a joint probability density function. Both bivariate models categorize SV and MV crashes into two subgroups, respectively. Among these models, BFM-I-2 exhibits better performance than BFM-II-2; it not only verifies the positive correlations between SV and MV crashes but also highlights substantial differences in the key contributing factors across the two crash types. Traffic volume exerts opposing effects on the two subgroups of SV crashes, indicating that unobserved heterogeneity may explain the inconsistent findings regarding its influence in previous research. For MV crashes, the two subgroups share few common risk factors: those in Component 1 are predominantly associated with traffic flow conditions, whereas Component 2 is largely affected by road geometry. These findings can support developing tailored freeway safety interventions.</p>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2026 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/8328602","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148942591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnosing Competition and Cooperation Potentials in Multilayer Rail Networks: A Spatial Framework and Evidence From Pearl River Delta, China","authors":"Huazhen Lin,&nbsp;Ruihua Xu","doi":"10.1155/atr/2971392","DOIUrl":"https://doi.org/10.1155/atr/2971392","url":null,"abstract":"<p>Emerging multilayer rail networks in metropolitan areas, comprising multiple service types and suppliers, pose new spatial challenges for operation and governance due to underlying cooperative and competitive dynamics. These challenges increase the need to diagnose the spatial distribution and intensity of interactions among coexisting services. Yet existing interpretation of competitive–cooperative dynamics and available diagnostic methodologies remain insufficient for addressing operational and governance challenges. To address this gap, by extending the concept of coopetition to multilayer rail networks, this study reveals that the substitutability and complementarity embedded in network spatial configurations shape the potential of coopetitive relations between operators. Based on this perspective, we reformulate the problem of measuring networkwide coopetition potential as pairwise relations between rail lines and propose two novel indicators, the network substitutability index (NSI) and the network complementarity index (NCI), to quantify the spatial potential for competition and cooperation between rail lines. To compute these indicators, a bipartite graph–based framework is developed to explicitly represent interline station substitutions and connections, together with an algorithm for efficiently identifying and aggregating relevant origin–destination (OD) relations. In the empirical application of metro and intercity rail systems in the Pearl River Delta (PRD) of Guangdong Province, China, the indicators are computed for all relevant line pairs to enable a systematic, networkwide diagnosis of substitutability and complementarity patterns. Empirical results demonstrate the framework’s applicability and reveal distinct spatial patterns of substitutability and complementarity. These patterns provide a basis for identifying corridors where coordination, differentiation, or regulatory attention may be prioritized, thereby informing service planning and integration-oriented governance in multilayer railway systems.</p>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2026 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/2971392","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148942459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Bibliometric Analysis of WoS-Indexed Intelligent Transportation Systems Research: Trends, Hotspots, and Emerging AI-Related Themes (1993–2024)","authors":"Ting Li","doi":"10.1155/atr/1557242","DOIUrl":"https://doi.org/10.1155/atr/1557242","url":null,"abstract":"<p>Intelligent transportation systems (ITS) have developed into an interdisciplinary research domain shaped by advances in sensing, communications, traffic modeling, data analytics, and artificial intelligence (AI). This study provides a domain-based bibliometric analysis of ITS research indexed in the Web of Science Core Collection. The main dataset was constructed from a WoS-only and domain-based search using the query TS = (“Intelligent Transportation System^∗”), which retrieved publications explicitly indexed with the term “Intelligent Transportation System(s).” After applying the SCIE index, document-type, and year filters, the final core dataset included 7813 publications from 1993 to 2024. Bibliometrix/Biblioshiny, VOSviewer, and complementary visualization tools were used to examine annual publication trends, disciplinary distribution, source journals, country and institutional patterns, citation-based intellectual foundations, and keyword evolution. To address the potential limitation of the single-query design, an expanded ITS-related sensitivity dataset was also constructed using broader terms such as smart transportation, smart mobility, connected vehicles, autonomous driving, traffic prediction, vehicular networks, V2X, Internet of Vehicles, transportation cyber-physical systems, and AI-based traffic management. The results show that WoS-indexed ITS research experienced rapid growth, with an average annual growth rate of 25.86% in the core dataset. China, the United States, and India were the most productive countries in the main dataset, while IEEE Transactions on Intelligent Transportation Systems, IEEE Access, Sensors, and IEEE Transactions on Vehicular Technology were major publication outlets. Citation analyses indicate that traffic flow forecasting, data-driven ITS, autonomous driving, connected vehicles, and Internet of Vehicles research form important intellectual foundations of the field. Keyword evolution suggests a gradual shift within the WoS-indexed ITS corpus from infrastructure-oriented and route-planning topics toward traffic modeling, intelligent control, connected mobility, and emerging AI-related themes such as deep learning, graph neural networks, federated learning, autonomous driving, and transformers. The expanded sensitivity dataset contained 27,261 records and showed broadly consistent temporal, geographic, source-level, and thematic patterns. These results support the robustness of the main findings while confirming that the core dataset should be interpreted as an explicit ITS-domain corpus rather than as a comprehensive census of all AI-in-transportation research. Therefore, the AI-related findings reported in this study should be understood as themes emerging within WoS-indexed ITS publications, not as a complete map of AI applications in transportation.</p>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2026 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/1557242","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148942460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust Real-Time Detection of Small Traffic Signs in Adverse Weather Conditions Using GAHP-YOLOv8","authors":"Jinke Cheng,&nbsp;Xue Wang,&nbsp;Sheng Zhang,&nbsp;Suhuan Feng,&nbsp;Jingbo Fu,&nbsp;Qingkun He,&nbsp;Guo Li,&nbsp;Wenbin Wang,&nbsp;Senhui Liu","doi":"10.1155/atr/5546959","DOIUrl":"https://doi.org/10.1155/atr/5546959","url":null,"abstract":"<p>Traffic sign detection (TSD) is a fundamental component of intelligent transportation systems (ITS) and autonomous driving. Despite recent advancements in deep learning, robust TSD in real-world scenarios remains challenging due to the prevalence of small targets, complex environmental interference (e.g., adverse weather and varying illumination), and the strict requirement for real-time inference. To address these issues, this paper proposes GAHP-YOLOv8n, an efficient and robust TSD algorithm. First, a global attention mechanism (GAM) is integrated into the backbone network to enhance the extraction of critical features while suppressing background noise across both channel and spatial dimensions. Second, a specialized small object detection layer is introduced to construct a more effective multiscale feature pyramid, mitigating feature loss for distant targets. Finally, a novel lightweight feature extraction module, C2f-PKI, is designed to optimize multiscale representation and global context awareness while significantly reducing computational overhead. Experimental results demonstrate that GAHP-YOLOv8n achieves 80.5% [email protected] and 61.2% [email protected]:0.95. This represents a significant improvement of 7.2% and 4.9% over the baseline YOLOv8n, respectively, while maintaining a high inference speed of 189.2 FPS. Comprehensive comparisons validate that the proposed model outperforms current mainstream detectors (e.g., YOLOv9t and YOLOv10n), achieving an optimal trade-off between accuracy and real-time efficiency in complex traffic scenarios.</p>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2026 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/5546959","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148942461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Planning-Stage Digital-Twin-Oriented Virtual-City Microsimulation Framework for Evaluating Street Network Typology and Signal Control Performance","authors":"Sheng-Hung Hsueh,&nbsp;Ching-Chien Huang","doi":"10.1155/atr/1777360","DOIUrl":"https://doi.org/10.1155/atr/1777360","url":null,"abstract":"<p>Street network typology defines the structural envelope within which traffic signal control can operate. Planning-stage assessments often treat urban form, demand allocation, signal timing, and microsimulation as separate tasks, leaving an unresolved question: whether coordinated signal timing can offset, or only mitigate, the operational disadvantage associated with an unfavorable street layout. This study develops a planning-stage digital-twin-oriented virtual-city microsimulation framework for evaluating street network typology and coordinated signal control as a coupled infrastructure-control system. Here, digital-twin-oriented virtual-city microsimulation framework denotes an auditable what-if workflow based on a controlled virtual representation, not real-time synchronization, physical-system mirroring, or closed-loop operational control. Four typologies, namely, Traditional Grid, Traditional Superblock, Neo-Traditional Grid, and Neo-Traditional Superblock, are constructed in a common 240-ha virtual city under common land use, population, roadway hierarchy, zoning, demand logic, route-choice treatment, and timing-generation protocol. Signalized intersections are selected using warrant-based criteria, coordinated timings are generated with TRANSYT 7F, and the final performance is evaluated in PARAMICS. The results provide deterministic descriptive evidence that structural-package differences remain after a common timing-generation protocol. Under both center-attraction settings, the superblock package produces a 22-s higher mean stop delay than the corresponding grid package, while reducing the center-attraction share from 46% to 32% lowers the mean stop delay by 6 s within both structural families. Relative to the Traditional Superblock Network, the Neo-Traditional Grid Network reduces mean stop delay from 52 to 24 s and total stop delay from 301,184 to 123,552 s, whereas mean travel distance decreases by only 2.8%. The dominant residual penalty of coarse block structure is therefore interruption accumulation rather than geometric route extension. The contribution is a reproducible planning-stage protocol that converts microsimulation outputs into auditable evidence for topology-sensitive signal coordination and street-network design. The findings should be interpreted as deterministic structural-package contrasts rather than stochastic significance tests, single-factor causal estimates, or real-time operational digital-twin results.</p>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2026 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/1777360","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148942074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Integrated Framework on Temporal Dynamics and Spatial Heterogeneity for Police Enforcement Effects on Freeway Crashes","authors":"Zihao Wen,&nbsp;Juan C. Medina","doi":"10.1155/atr/4944775","DOIUrl":"https://doi.org/10.1155/atr/4944775","url":null,"abstract":"<p>Police enforcement constitutes a key element of traffic safety management efforts, yet existing studies have largely relied on system-wide and temporally aggregated analyses, limiting insight into spatial heterogeneity and dynamic enforcement effects. This study develops an integrated framework to examine the dynamic relationship between police enforcement and crashes at fine spatial and temporal resolutions, with the objective of characterizing spatial heterogeneity, temporal dynamic, and persistence of enforcement effects. The framework integrates resolution diagnostics, dynamic time-series models, and deep-learning verification. Effective sample size and impulse-response stability metrics are used to identify spatial and temporal resolutions that balance analytical precision with inferential reliability. Vector autoregression and structural vector autoregression models with impulse response functions quantify contemporaneous and lagged enforcement effects, while deep-learning models, including encoder–decoder architectures, transformers, and graph neural networks, provide independent directional verification under flexible nonlinear specifications. The results identified a consistently short-lived inverse citation–crash relationship across all segments, with substantial spatial heterogeneity and generally stronger enforcement responses in urban segments than in rural segments. The consistency between deep-learning sensitivity measures and impulse response estimates supports the robustness of these findings. The results provide actionable and practical insights into spatially and temporally targeted enforcement strategies, supporting data-driven police resource allocation to improve freeway safety.</p>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2026 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/4944775","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148943450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Distribution-Level Regularization for Robust Reinforcement Learning in Autonomous Driving","authors":"Hassanien Zanki,&nbsp;Árpád Huszák","doi":"10.1155/atr/6789335","DOIUrl":"https://doi.org/10.1155/atr/6789335","url":null,"abstract":"<p>In recent years, reinforcement learning (RL) has emerged as a powerful platform for developing autonomous driving systems. Nevertheless, while RL policies perform well under ideal conditions, they frequently exhibit unstable behavior when subjected to noisy observations or imperfect actuation factors that significantly compromise safety in real-world deployment. This study seeks to address these vulnerabilities by proposing distribution-level regularization (DLR). Rather than altering the underlying policy architecture, DLR introduces a training constraint that encourages consistent action distributions across both clean and perturbed inputs, facilitating straightforward integration into existing frameworks. The utility of this approach was tested by incorporating DLR into an on-policy proximal policy optimization (PPO) model across discrete- and continuous-action spaces, and further extending it to an off-policy soft actor-critic (SAC) model under continuous control, evaluating its performance across diverse robustness scenarios, including stochastic noise and actuator faults, in highway and intersection environments. The results provide evidence that DLR effectively minimizes collision frequency and extends stable driving duration without sacrificing performance under nominal conditions. These findings suggest that robust autonomous behavior may be achieved more efficiently through output-level constraints rather than through the development of increasingly complex architectures.</p>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2026 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/6789335","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148849074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vision-Based Queue Estimation and Prediction for Ramp Metering: A Hybrid Computer Vision and Queuing Theory Approach","authors":"Sushant Tiwari,&nbsp;Sanjay Luitel,&nbsp;Abbas Rashidi,&nbsp;Nikola Marković","doi":"10.1155/atr/2604474","DOIUrl":"https://doi.org/10.1155/atr/2604474","url":null,"abstract":"<p>Ramp metering is a critical tool for improving freeway efficiency, but its performance relies on real-time queue-state estimation. Conventional sensor-based methods (e.g., inductive loops) often fail under congestion, leading to unreliable queue data. This study proposes a hybrid computer vision and queuing theory framework to overcome these limitations. The computer vision-based framework integrates a segmentation model for ramp identification, object detection and tracking (YOLO + DeepSORT) for vehicle flow extraction, and a time-varying queuing model to compute queue length and delay. To support proactive control, a random forest-based predictor forecasts arrivals over a 10-min horizon. Evaluated on four metered ramps in Utah under diverse lighting and seasonal conditions, the system achieved 93.0% accuracy for queue-length estimation in clear conditions and 77%–87% under adverse weather conditions. The hybrid adaptive vision–queuing approach reduced annualized estimation error by 49% (queue length) and 30% (queuing time) compared to a vision-only baseline. By leveraging existing surveillance infrastructure, this work provides a scalable, robust solution for real-time ramp management, demonstrating the operational value of fusing vision-based sensing with analytical models for proactive ramp management.</p>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2026 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/2604474","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148783825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}